Unsupervised Representation Learning. Researches on unsupervised representation learning have achieved remarkable success. He et al. [1]} propose Momentum Contrast (MoCo) for learning representations from unlabeled images, and the learned features are shown to be useful for downstream tasks. Oord et al. [2]} present the noise contrastive learning with InfoNCE loss, and show that InfoNCE loss maximizes the lower bound of mutual information between related representations. Tian et al. [3]} introduce a Contrastive Multiview Coding method for unsupervised learning of multi-view (or multi-modal) data by using the multiple views of a same example as positive pairs. Contrastive learning is also exploited for learning representations of 3D objects by Sanghi [4]} et al., where the learned representation is shown to be useful for retrieving different views of a rigid object or similar objects.
